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Burnley enter the new season holding ‘the best of the rest’ crown following their highest place finish since 1974 last time out. The club are breaking new ground in their recent history, with a third consecutive season in the top flight plus the visit of European football to Turf Moor for the first time since the mid-sixties.

According to StatsBomb’s underlying numbers, Burnley’s finishing position was reasonably well-merited, with the ninth best expected goal difference in the league. That performance was powered by the sixth best defence in the league in both their outcomes and expectation. Fifty-four points wouldn’t usually be enough to secure seventh but Burnley can hardly be blamed for the deficiencies of others; eighth place Everton sitting on forty-nine points tells the story well on that front.

With all of the above considered though, seventh seems like the very top of Burnley’s potential outcomes given they don’t have the financial means to vault into challenging the top-six, so where do they go from here?

StatsBomb’s new shot event data includes the position of the players at the moment of the shot and it’s fair to say that Burnley keep the data collectors busy. StatsBomb’s expected goal model includes all of that information and puts their average expected goal conceded per shot at 0.084, which is the lowest in the league by some distance. It’s an impressive feat and it is what powers their defensive performance as only Stoke City and West Ham conceded more shots last season, which would usually put you amongst the worst defensive teams in the league.

Sean Dyche has instilled a defensive system based around forcing their opponent’s to take shots from poor locations and getting as many bodies between those shots and the goal as possible, even if those bodies don’t necessarily pressure the shot-taker. To give an overall picture of this, Burnley’s shots conceded rank:

Third longest distance from goal (the fundamental building block of expected goals).

Lowest proportion of shots where only the goalkeeper was between the shot-taker and the goal.

Fourth highest in density of players between the shot-taker and the goal.

Seventh highest in proportion of shots under pressure.

Ninth shortest distance between the shot-taker and the closest defender.

No other team comes close to putting all of those numbers together and when you add it all up you get a potent defensive cocktail that sees the highest proportion of blocked shots in the league and the lowest proportion of shots on target. Even with all that added to the model melting pot, Burnley still out-performed their expected goal figures to the tune of 12 goals, which is similar result to traditional expected goal models. However, StatsBomb’s model rates them more highly relative to the rest of the league. There was still some air in their numbers, but the process has a more-solid footing.

While their bunker-like approach might sound reminiscent of your ‘typical’ English defensive style, Burnley actually differ markedly further up the pitch where they apply a blanket of pressure on their opponents. The average distance of their defensive actions sat at a league average level, as did their opponent’s pass completion rate.

Burnley counter-pressed at a league average intensity, sitting tenth overall. Based on a simple model of the strong relationship between counter-pressing and possession, Burnley counter-pressed more than any other team relative to their level of possession. The midfield pair of Steven Defour and Jack Cork led their defensive-pressing efforts, with able support from their wingers and attacking midfielders.

Defence has been the bedrock of this Burnley side and there is no reason to expect 2018/19 to be any different.

Attacking Concerns

On the attacking end, Burnley were thirteenth in both shots (10.6) and expected goals (1.1) per game, which you can likely surmise meant their expected goal per shot was distinctly league average. They actually under-performed their expected goals to the tune of six goals, which caused them particular problems at home where they were down seven goals against expectation.

Chris Wood provided very good numbers in his debut season, contributing 0.45 expected goals per 90, which was tenth highest of players who played over 900 minutes and third highest of those not at one of the top-six. His shot map illustrates his fondness for the central area of the box, with his expected goals per shot sitting fourth of players taking more than one shot per 90 minutes. Even his shots from outside of the penalty area were reasonably high quality, with two of them coming with the keeper out of position and no defender blocking his path to goal, one of which yielded a goal against Crystal Palace on his full debut.

While Wood’s numbers were very good, there was a drop-off in goal-scoring contribution across the rest of the squad; Ashley Barnes and Welsh legend Sam Vokes were contributing at a 1 in 3 game rate in both expectation and actual output, with the midfield ranks providing limited goal-scoring support.

Wood’s medial ligament injury just before Christmas and two-month absence coincided with Burnley’s attack dropping below one expected goal per game. This was compounded by a poor run on the defensive side as well, leading to them collecting just 4 points in 9 games with Wood out of the starting eleven.

Gudmundsson carried the creative burden, with Brady chipping in when in the team but both relied heavily on set pieces when examining their expected assist contribution. Burnley were likely a touch unfortunate to not score more from dead-ball situations as their underlying process was good. However, there is certainly a lot of room for improvement in creativity from open-play.

With perhaps less scope for improvement on the defensive side, Burnley could do with improving their attacking output to really establish themselves in the top-half of the table. If Chris Wood can remain healthy and maintain his form then that would certainly help but ideally you would want to see an attack that is less reliant on one individual.

Transfers

From a departures point of view, Scott Arfield is the only player to leave who contributed reasonably significant minutes last season. After going most of the summer without an incoming transfer on the horizon, things have got busier over the last few days of the window

Ben Gibson arrives from one of the better defensive teams in the Championship to provide competition and depth to the central defensive ranks. He could potentially ease Ben Mee out of the starting eleven and form a peak-age partnership with James Tarkowski once he is up to speed with Burnley’s defensive system.

Another arrival from the Championship is Matej Vydra, whose 21 goals last season saw him top the scoring charts, although his total was inflated by 6 penalties. His 15 non-penalty goals put him joint-fourth across the season in terms of volume at a rate of 0.50 goals per 90. However, his goal-scoring record over his career could be charitably described as ‘patchy’, while his two previous stints in the Premier League were mostly spent on the side-lines. Those concerns aside, the hope is that he can form an effective partnership with Wood and provide more creativity and a greater goal-threat than Jeff Hendrick, which is a practically subterranean low-bar.

Burnley have a recent history of reasonably successful recruitment from the Championship and are seemingly following that model again with the signings of Gibson and Vydra. Adding a more creative option in open-play looks like the area where they could have clearly upgraded the existing squad. It’s hard not to wonder whether they could have used their success last season and the draw of European football to improve their first-eleven. That said, being financially prudent isn’t the worst strategy and has seen them progress over recent years, so it’s hard to be too critical.

Where Do We Go from Here?

Burnley’s prospects this season are likely closely-tied to whether they qualify for the Europa League group stage. Injuries aside, they played essentially a first-choice team against Aberdeen, so are clearly aiming to progress. İstanbul Başakşehir are ranked 66th in Europe based on their Elo ranking, with Burnley in 53rd, so their tie is expected to be evenly-balanced.

Burnley ran with the most settled line-up in the league by a wide margin last season and squad depth is a major concern with the potential Thursday-Sunday grind that comes with Europa League qualification. Add in whatever the league cup is called this year and the next few months could be perilous.

The range of potential outcomes for this Burnley squad seems quite broad and the bookmakers are certainly unconvinced. On one end of the scale they could secure another top-half finish and put together a European adventure to bore the next generation of fans with, while on the flip-side they could struggle with the extra strain on the squad and find themselves at the wrong end of the table. The backbone of their past two campaigns has been their form in the first half of the season, which has kept them well-outside the relegation battle. That wasn’t the case in 2014/15 when they spent the entire season in the bottom four and struggled for goals when they needed to put wins on the board. A similar scenario playing out this term amidst a potentially stronger bottom-half could well be in play heading into 2019.

However, Burnley have made a habit of defying expectations and even have the opportunity to expand their exploits abroad this year. We’ll see if Sean Dyche can weave further sorcery from his spell-book.

Like this:

Quantifying passing skill has been a topic that has gained greater attention over the past 18 months in public analytics circles, with Paul Riley, StatsBomb and Played off the Park regularly publishing insights from their passing models. I talked a little about my own model last season but only published results on how teams disrupted their opponents passing. I thought delving into the nuts and bolts of the model plus reporting some player-centric results would be a good place to start as I plan to write more on passing over the next few months.

Firstly, the model quantifies the difficulty of an open-play pass based on its start and end location, as well as whether it was with the foot or head. So for example, relatively short backward passes by a centre back to their goalkeeper are completed close to 100% of the time, whereas medium-range forward passes from out-wide into the centre of the penalty area have pass completion rates of around 20%.

The data used for the model is split into training and testing sets to prevent over-fitting. The Random Forest-based model does a pretty good job of representing the different components that drive pass difficulty, some of which are illustrated in the figure below (also see the appendix here for some further diagnostics).

Comparison between expected pass completion rates from two different passing models and actual pass completion rates based on the start and end location of an open-play pass. The horizontal dimension is orientated from left-to-right, with zero designating the centre of the pitch. The dashed lines in the vertical dimension plots show the location of the edge of each penalty area. Data via Opta.

One slight wrinkle with the model is that it has trouble with very short passes of less than approximately 5 yards due to the way the data is collected; if a player attempts a pass and an opponent in his immediate vicinity blocks it, then the pass is unsuccessful and makes it looks like such passes are really hard, even though the player was actually attempting a much longer pass. Neil Charles reported something similar in his OptaPro Forum presentation in 2017. For the rest of the analysis, such passes are excluded.

None shall pass

That gets some of the under-the-hood stuff out of the way, so let’s take a look at ways of quantifying passing ‘skill’.

Similar to the concept of expected goals, the passing model provides a numerical likelihood of a given pass being completed by an average player; deviations from this expectation in reality may point to players with greater or less ‘skill’ at passing. The analogous concept from expected goals would be comparing the number of goals scored versus expectation and interpreting this as ‘finishing skill‘ or lack there of. However, when it comes to goal-scoring, such interpretations tend to be very uncertain due to significant sample size issues because shots and goals are relatively infrequent occurrences. This is less of a concern when it comes to passing though, as many players will often attempt more passes in a season than they would take shots in their entire career.

Another basic output of such models is an indication of how adventurous a player is in their passing – are they playing lots of simple sideways passes or are they regularly attempting defense-splitting passes?

The figure below gives a broad overview of these concepts for out-field players from the top-five leagues (England, France, Germany, Italy and Spain) over the past two seasons. Only passes with the feet are included in the analysis.

Passing ‘skill’ compared to pass difficulty for outfield players from the past two seasons in the big-five leagues, with each data point representing a player who played more than 3420 minutes (equivalent to 38 matches) over the period. The dashed lines indicate the average values across each position. Foot-passes only. Data from Opta.

One of the things that is clear when examining the data is that pulling things apart by position is important as the model misses some contextual factors and player roles obviously vary a huge amount depending on their position. The points in the figure are coloured according to basic position profiles (I could be more nuanced here but I’ll keep it simpler for now), with the dashed lines showing the averages for each position.

In terms of pass difficulty, midfielders attempt the easiest passes with an average expected completion of 83.2%. Forwards (81.6%) attempt slightly easier passes than defenders (81.4%), which makes sense to me when compared to midfielders, as the former are often going for tough passes in the final third, while the latter are playing more long passes and crosses.

Looking at passing skill is interesting, as it suggest that the average defender is actually more skilled than the average midfielder?!? While the modern game requires defenders to be adept in possession, I’m unconvinced that their passing skills outstrip midfielders. What I suspect is happening is that passes by defenders are being rated as slightly harder than they are in reality due to the model not knowing about defensive pressure, which on average will be less for defenders than midfielders.

Forwards are rated worst in terms of passing skill, which is probably again a function of the lack of defensive pressure included as a variable, as well as other skills being more-valued for forwards than passing e.g. goal-scoring, dribbling, aerial-ability.

Pass muster

Now we’ve got all that out of the way, here are some lists separated by position. I don’t watch anywhere near as much football as I once did, so really can’t comment on quite a few of these and am open to feedback.

Note the differences between the players on these top-ten lists are tiny, so the order is pretty arbitrary and there are lots of other players that the model thinks are great passers who just missed the cut.

First-up, defenders: *shrugs*.

In terms of how I would frame this, I wouldn’t say ‘Faouzi Ghoulam is the best passer out of defenders in the big-five leagues’. Instead I would go for something along the lines of ‘Faouzi Ghoulam’s passing stands out and he is among the best left-backs according to the model’. The latter is more consistent with how football is talked about in a ‘normal’ environment, while also being a more faithful presentation of the model.

Looking at the whole list, there is quite a range of pass difficulty, with full-backs tending to play more difficult passes (passes into the final third, crosses into the penalty area) and the model clearly rates good-crossers like Ghoulam, Baines and Valencia. Obviously that is a very different skill-set to what you would look for in a centre back, so filtering the data more finely is an obvious next step.

Defenders (* denotes harder than average passes)

Name

Team

xP rating

Pass difficulty

Faouzi Ghoulam

Napoli

1.06

80.3*

Leighton Baines

Everton

1.06

76.5*

Stefan Radu

Lazio

1.06

82.1

Thiago Silva

PSG

1.06

91.0

Benjamin Hübner

Hoffenheim

1.05

84.4

Mats Hummels

Bayern Munich

1.05

86.0

Kevin Vogt

Hoffenheim

1.05

87.4

César Azpilicueta

Chelsea

1.05

83.4

Kalidou Koulibaly

Napoli

1.05

87.8

Antonio Valencia

Manchester United

1.05

80.0*

On to midfielders: I think this looks pretty reasonable with some well-known gifted passers making up the list, although I’m a little dubious about Dembélé and Fernandinho being quite this high up. Iwobi is an interesting one and will keep James Yorke happy.

Fàbregas stands-out due to his pass difficulty being well-below average without having a cross-heavy profile – nobody gets near him for the volume of difficult passes he completes.

Midfielders (* denotes harder than average passes)

Name

Team

xP rating

Pass difficulty

Cesc Fàbregas

Chelsea

1.06

79.8*

Toni Kroos

Real Madrid

1.06

88.1

Luka Modric

Real Madrid

1.06

85.9

Arjen Robben

Bayern Munich

1.05

79.6*

Jorginho

Napoli

1.05

86.8

Mousa Dembélé

Tottenham Hotspur

1.05

89.9

Fernandinho

Manchester City

1.05

87.2

Marco Verratti

PSG

1.05

87.3

Alex Iwobi

Arsenal

1.05

84.9

Juan Mata

Manchester United

1.05

84.5

Finally, forwards AKA ‘phew, it thinks Messi is amazing’.

Özil is the highest-rated player across the dataset, which is driven by his ability to retain possession and create in the final third. Like Fàbregas above, Messi stands out for the difficulty of the passes he attempts and that he is operating in the congested central and half-spaces in the final third, where mere mortals (and the model) tend to struggle.

In terms of surprising names: Alejandro Gomez appears to be very good at crossing, while City’s meep-meep wide forwards being so far up the list makes we wonder about team-effects.

Also, I miss Philippe Coutinho.

Forwards (* denotes harder than average passes)

Name

Team

xP rating

Pass difficulty

Mesut Özil

Arsenal

1.07

82.9

Eden Hazard

Chelsea

1.05

81.9

Lionel Messi

Barcelona

1.05

79.4*

Philippe Coutinho

Liverpool

1.04

80.6*

Paulo Dybala

Juventus

1.03

84.8

Alejandro Gomez

Atalanta

1.03

74.4*

Raheem Sterling

Manchester City

1.03

81.6*

Leroy Sané

Manchester City

1.03

81.9

Lorenzo Insigne

Napoli

1.03

84.3

Diego Perotti

Roma

1.02

78.4*

Finally, the answer to what everyone really wants to know is, who is the worst passer? Step-forward Mario Gómez – I guess he made the right call when he pitched his tent in the heart of the penalty area.

Pass it on

While this kind of analysis can’t replace detailed video and live scouting for an individual, I think it can provide a lot of value. Traditional methods can’t watch every pass by every player across a league but data like this can. However, there is certaintly a lot of room for improvement and further analysis.

A few things I particularly want to work on are:

Currently there is no information in the model about the type of attacking move that is taking place, which could clearly influence pass difficulty e.g. a pass during a counter-attacking situation or one within a long passing-chain with much slower build-up. Even if you didn’t include such parameters in the model, it would be a nice means of filtering different pass situations.

Another element in terms of context is attempting a pass after a dribble, especially given some of the ratings above e.g. Hazard and Dembélé. I can envisage the model somewhat conflates the ability to create space through dribbling and passing skill (although this isn’t necessarily a bad thing depending on what you want to assess).

Average difficulty is a bit of a blunt metric and hides a lot of information. Developing this area should be a priority for more detailed analysis as I think building a profile of a player’s passing tendencies would be a powerful tool.

You’ll have probably noticed the absence of goalkeepers in the results above. I’ve left them alone for now as the analysis tends to assign very high skill levels to some goalkeepers, especially those attempting lots of long passes. My suspicion is that long balls up-field that are successfully headed by a goalkeeper’s team-mate are receiving a bit too much credit i.e. yes the pass was ‘successful’ but that doesn’t always mean that possession was retained after the initial header. That isn’t necessarily the fault of the goalkeeper, who is generally adhering to the tactics of their team and the match situation but I’m not sure it really reflects what we envisage as passing ‘skill’ when it comes to goalkeepers. Discriminating between passes to feet and aerial balls would be a useful addition to the analysis here.

Using minutes as the cut-off for the skill ratings leaves a lot of information on the table. The best and worst passers can be pretty reliably separated after just a few hundred passes e.g. Ruben Loftus-Cheek shows up as an excellent passer after just ~2000 minutes in the Premier League. Being able to quickly assess young players and new signings should be possible. Taking into account the number of passes a player makes should also be used to assess the uncertainty in the ratings.

I’ve gone on enough about this, so I’ll finish by saying that any feedback on the analysis and ratings is welcome. To facilitate that, I’ve built a Tableau dashboard that you can mess around with that is available from here and you can find the raw data here.

Analysing centre backs is a subject likely to provoke either a shrug or a wistful smile from an analytics practitioner. To varying degrees, there are numbers and metrics aplenty for other positions but in public analytics at least, development has been limited and a genuine track record of successful application is yet to be found. If centre back analysis is the holy grail of public football analytics, then the search thus far has been more Monty Python than Indiana Jones.

One of the major issues with centre back analysis is that positioning isn’t measured directly by on-ball event data and any casual football watcher can tell you that positioning is a huge part of the defensive art. Tracking data would be the ideal means to assess positioning but it comes at a high-cost both computationally and technically, while having a much smaller coverage in terms of leagues than simpler event data provision.

StatsBomb’s new pressure event data serves as a bridge between the traditional on-ball event data and the detailed information provided by tracking data, offering a new prism to investigate the style and effectiveness of centre backs. While it won’t provide information on what a defender is up to when he is not in the immediate vicinity of the ball, it does provide extra information on how they go about their task.

Starting at the basic counting level, centre backs averaged six pressure actions per ninety minutes in the Premier League last season. Tackles and interceptions clock in at 0.8 and 1.3 per 90 respectively, which immediately illustrates that pressure provides a great deal more information to chew on when analysing more ‘proactive’ defending. I’m classing clearances and blocking shots as ‘reactive’ given they mostly take place in the penalty area and are more-directly driven by the opponent, while aerial duels are a slightly different aspect of defending that I’m going to ignore for the purposes of this analysis.

The figure below maps out where these defensive actions occur on the pitch and is split between left and right centre backs. Pressure actions typically occur in wider areas in the immediate vicinity of the penalty area, with another peak in pressure just inside the top corner of the 18-yard box. This suggests that centre backs don’t engage too high up the pitch in terms of pressure and are generally moving out towards the flanks to engage opponents in a dangerous position and either slow-down an attack, cut down an attackers options or directly contest possession.

Maps illustrating the location of pressure actions, interceptions and tackles by centre backs in the 2017/18 EPL season. Top row is for left-sided centre backs and the bottom row is for right-sided centre backs.

The location of pressure actions is somewhat similar to the picture for interceptions, although the shape of the latter is less well-defined and tends to extend higher up the pitch. Tackles peak in the same zone just outside the top corners of the penalty area but are also less spatially distinct. Tackles also peak next to the edge of the pitch, a feature that is less distinct in the pressure and interception maps.

Partners in Crime

The number of pressure actions a centre back accumulates during a match will be driven by their own personal inclinations and role within the team, as well as the peculiarities of a given match and season e.g. the tactics of their own team and the opposition or the number of dangerous opportunities their opponent creates. The figure below explores this by plotting each individual centre back’s pressure actions per ninety minutes against their team name. The team axis is sorted by the average number of pressure actions the centre backs on each team make over the season.

Pressure actions per 90 minutes by centre backs in the 2017/18 EPL season (minimum 900 minutes played) by team. Team axis is sorted by the weighted average number of pressure actions the centre backs on each team make over the season.

At the top end of the scale, we see Arsenal and Chelsea, two teams that regularly played a back-three over the past season. Nacho Monreal and César Azpilicueta led the league in pressure actions per ninety minutes by a fair distance and it appears the additional cover provided by playing in a back-three and their natural instincts developed as full backs meant they were frequently putting their opponents under pressure. Manchester United top the list in terms of those predominantly playing with two centre backs, with all of their centre backs applying pressure at similar rates.

At the other end of the scale, Brighton and Leicester’s centre backs appear to favour staying at home in general. Both though are clear examples of there being an obvious split between the number of pressure actions by the primary centre backs on a team, with one being more aggressive while the other presumably holds their position and plays a covering role. This division of roles is perhaps most clearly demonstrated by Chelsea’s centre backs, with Azpilicueta and Antonio Rüdiger as the side centre backs being more proactive than their counter-part in the central defensive slot (Cahill or Christensen).

Liverpool’s improved defensive performance over the course of the season has been attributed to a range of factors, with the signing of Virgil Van Dijk for a world-record fee garnering much of the credit. Intriguingly, his addition to the Liverpool backline has seemingly offered a significant contrast to the club’s incumbents, who all favoured a slightly greater than average number of pressure actions. Furthermore, Van Dijk ranked towards the bottom of the list in terms of pressure actions for Southampton (4.5 per 90) as well, with his figure for Liverpool (3.7 per 90) representing a small absolute decline. As an aside, Van Dijk brings a lot to the table in terms of heading skills, where he ranks highly for both total and successful aerial duels, so he is still an active presence in this aspect, while being a low-event player in others.

Centre backs are often referred to as a partnership and the above illustrates how defensive units often setup to complement each others skill sets and attempt to become greater than the sum of their parts.

The Thompson Triangle

Mark Thompson has led the way in terms of public analytics work on centre backs and has advocated for stylistic-driven evaluations as the primary means of analysis, which can then be built on with more traditional scouting. Pressure actions add another string to this particular bow and the figure below contrasts the three proactive defensive actions discussed earlier. Players in different segments of the triangle are biased towards certain actions, with those in the corners being more strongly inclined towards one action over the other two.

Comparison of player tendencies in terms of ‘proactive’ defensive actions in the 2017/18 EPL season (minimum 900 minutes played). Apologies for triggering any flashbacks to chemistry classes. Click figure to open in new window.

There is a lot to pour over in the figure, so I’ll focus on defenders who are most inclined towards pressure actions. One clear theme is that such centre backs frequently featured on the sides of a back-three. Ryan Shawcross is unusual in this aspect given he was generally the middle centre back in Stoke’s back-three, as well as the right centre back in a back four. Ciaran Clark at Newcastle and Kevin Long at Burnley are the only players who featured mostly as one of two centre backs, with their partner adopting a more reserved role.

The additional cover provided by a back-three system and the frequent requirement for the player on the flanks to pull wide and cover in behind their wing-back seemingly plays a large part in determining the profile of centre backs. This illustrates the importance of considering team setup in determining a defenders profile and should feed into any recruitment process alongside their individual inclinations.

The analysis presented provides descriptive metrics and illustrations of the roles played by centre backs and is very much a first look at this new data. While we can’t gain definitive information on positioning without constant tracking of a player, the pressure event data provides a new lens to evaluate centre backs and significantly increases the number of defensive actions that can be evaluated further. Armed with such information, these profiles can be built upon with further data-driven analysis and combined with video and in-person scouting to build a well-rounded profile on the potential fit of a player.

When asked how his Liverpool team would play by the media horde who greeted his unveiling as manager two years ago, Jürgen Klopp responded:

We will conquer the ball, yeah, each fucking time! We will chase the ball, we will run more, fight more.

The above is a neat synopsis of Klopp’s preferred style of play, which focuses on pressing the opponent after losing the ball and quickly transitioning into attack. It is a tactic that he successfully deployed at Borussia Dortmund and one that he has employed regularly at Liverpool.

Liverpool’s pass disruption map for the past three seasons is shown below. Red signifies more disruption (greater pressure), while blue indicates less disruption (less pressure). In the 2015/16 and 2016/17 seasons, the team pressed effectively high up the pitch but that has slid so far this season to a significant extent. There is some disruption in the midfield zone but at a lower level than previously.

Liverpool’s zonal pass completion disruption across the past three seasons. Teams are attacking from left-to-right, so defensive zones are to the left of each plot. Data via Opta.

The above numbers are corroborated by the length of Liverpool’s opponent possessions increasing by approximately 10% this season compared to the rest of Klopp’s reign. Their opponents so far this season have an average possession length of 6.5 seconds, which is lower than the league average but contrasts strongly with the previous figures that have been among the shortest in the league.

Examining their pass disruption figures game-by-game reveals further the reduced pressure that Liverpool are putting on their opponents. During 2015/16 and 2016/17, their average disruption value was around -2.5%, which they’ve only surpassed once in Premier League matches this season, with the average standing at -0.66%.

The Leicester match is the major outlier and examining their passing further indicates that the high pass disruption was a consequence of them attempting a lot of failed long passes. This is a common response to Liverpool’s press as teams go long to bypass the pressure.

Liverpool’s diminished press is likely a deliberate tactic that is driven by the added Champions League matches the team has faced so far this season. The slightly worrisome aspect of this tactical shift is that Liverpool’s defensive numbers have taken a hit.

In open-play, Liverpool’s expected goals against figure is 0.81 per game, which is up from 0.62 last season. Furthermore, their expected goals per shot has risen to 0.13 from 0.11 in open-play. To add further defensive misery, Liverpool’s set-piece woes (specifically corners) have actually got worse this season. The team currently sit eleventh in expected goals conceded this season, which is a fall from fifth last year.

This decline in underlying defensive performance has at least been offset by a rise on the attacking side of 0.4 expected goals per game to 1.78 this season. Overall, their expected goal difference of 0.79 this season almost exactly matches the 0.81 of last season.

Liverpool’s major problem last season was their soft under-belly but they were often able to count on their pressing game denying their opponents opportunities to exploit it. What seems to be happening this season is that the deficiencies at the back are being exploited more with the reduced pressure ahead of them.

With the season still being relatively fresh, the alarm bells shouldn’t be ringing too loudly but there is at least cause for concern in the numbers. As ever, the delicate balancing act between maximising the sides attacking output while protecting the defense is the key.

Klopp will be searching for home-grown solutions in the near-term and a return to the familiar pressing game may be one avenue. Given the competition at the top of the table, he’ll need to find a solution sooner rather than later, lest they be left behind.

Liverpool enter the season with aspirations of challenging for the title after an at times hugely promising and exciting first full season under Jürgen Klopp. The prospect of European adventures returning on Tuesday or Wednesday nights is tantalizing close providing they negotiate their Champions League qualifying round.

The story so far

Liverpool’s tally of 76 points last season was their joint-third best tally over the last decade and only their second top-four finish since the Benitez years. In fact, after a run of four top-four finishes, Liverpool haven’t registered back-to-back Champions League qualifications since Rafa left and have on average finished sixth during that time with 65 points on the board.

With the above in mind, it’s tempting to view a season of consolidation as the priority for the coming season, alongside beginning to re-establish the team as a European force. Liverpool’s underlying performance last season is encouraging, with their goal return reasonably in-line with expectation and their expected goal difference placing them well in contention for a title push.

Drilling further into their expected goal numbers, sees a team that experienced fluctuating under-lying performance over the course of the season with a significant decline once 2017 was rung in. The graphic below illustrates this alongside a longer-term outlook encompassing the past five seasons.

The heights of 2016/17 are close to those of the Suárez-powered team under Rodgers, while the low-point is more in-line with Klopp’s early tenure at the club. The past season thus illustrated that the team was capable of title-contending performances at times but also switched to a team competing for the fourth-place trophy at best.

Upping the pace

Closer examination of the downturn in performance using my ‘team strategy analysis‘ shows a drying up of shot generation via high-quality chances born of fast-paced attacks from deep and after midfield-transitions.

Sadio Mané was evidently missed due to AFCON duties and injury over the latter half of the season and this is borne out by the numbers. According to my model, he was second best in the EPL (0.11 per 90) in terms of xG-contribution (the sum of expected goals and assists) from fast-paced attacks following a midfield-transition. For fast-attacks from deep, he ranked sixth for xG-contribution (0.12 per 90).

Thankfully, Mohamed Salah, the club’s major acquisition so far, brings complementary qualities to the table and adds much-needed depth to the wide-forward ranks. James Yorke of this parish has already praised the signing earlier this summer and my only addition is that Salah showed up quite highly for xG-contribution (0.07 per 90, ranking eleventh in Serie A) for fast-paced attacks following a midfield-transition. The addition of Salah improves what was already a healthy front-line attack.

Defensive issues

According to the Objective Football website run by Benjamin Pugsley, Liverpool conceded just 8.1 non-penalty shots per game, ranking second over the past eight seasons behind a Pep-infused Manchester City last year. Shots-on-target conceded (3.0 per game) told a similar story, ranking joint-sixth over the same period. However, they combined these extraordinary shot-suppression numbers with the highest expected goals per shot in the league (0.11), which is the worst value I have over the past five seasons. When Liverpool conceded shots, they were of high quality, which ultimately saw them sit fifth in terms of expected goals against last season.

Klopp’s tactical system deserves credit for melding a highly exciting attack with strong defensive aspects in terms of shot-suppression. The optimistic take here is that tweaks and a greater familiarity with his counter-pressing tactics could bring about improvements in shot quality conceded, thereby seeing better defensive numbers. It’s worth noting the period during November and December 2016 when their expected goals against was the lowest it has been consistently over a 19-game span in the past five seasons, so the current squad is capable of sustained excellence in this realm.

The pursuit of Virgil van Dijk does suggest that the club are aiming to recruit a new starting centre-back. That saga remains running at the time of writing as the world waits to find out just how costly a single ice cream can be. Centre-back depth is an issue that needs to be rectified; Lucas Leiva made six appearances as a centre-back last term is all the evidence needed for that statement.

The other aspect of Liverpool’s defense that could improve is in the goalkeeping stakes. From a pure shot-stopping perspective, Karius has the best pedigree; in my goalkeeper shot-stopping analysis, Karius came 31st across the data-set with a rating of 91%, which is a pretty decent indication that he is an above-average shot-stopper. Mignolet fared much worse with a ranking of just 25%, which puts him at best as an average shot-stopper during his Liverpool career to date. I haven’t looked at numbers for the Championship but Mark Taylor’snumbers for Ward at Huddersfield were not encouraging. Playing Karius would be a bold move by Klopp given his limited exposure to English football thus far but Mignolet doesn’t provide much confidence either -personally, I would go with Karius.

Title talk

If I’ve learnt anything while sifting through the data for this preview, it’s that Manchester City should be strong favourites for the title this coming season.

Can Liverpool challenge them, while also competing in Europe? At present, I’d side with no given the depth issues of last season have yet to be addressed and the remaining questions marks in terms of the defense.

Liverpool’s other transfer saga involving Naby Keita could be a game-changer given that he could have a transformative impact on the team’s midfield but the likelihood of him signing appears to be receding by the day. Midfield depth is also potentially an issue unless Klopp is happy to rely on youth to cover midfield absentees over the season.

With potentially five teams in the Champions League group stages, progress to the latter rounds could have a strong bearing on league form post-Christmas. Six into four is likely the maths heading into the new season and Liverpool should be well in the mix.

At the recent OptaPro Analytics Forum, I was honoured to be selected to present for a second time to an audience of analysts and other representatives from the sporting industry. My aim was to explore the multifaceted approaches employed by teams using cluster analysis of possession chains.

My thinking was that this could be used to assess the strengths and weaknesses of teams in both attack and defense, which could be used for opposition scouting. The results can also be used to evaluate how well players contribute to certain styles of play and potentially use this in recruitment.

The video of the presentation is below, so go ahead and watch it for more details. The slides are available here and I’ve pulled out some of the key graphics below.

The main types of attacking moves that result in shots are in the table below. I used the past four full English Premier League seasons plus the current 2016/17 season for the analysis here but an obvious next step is to expand the analysis across multiple leagues.

Below is a comparison of the efficiency (in terms of shot conversion) and frequency of these attack types. The value of regaining the ball closer to goal and quickly transitioning into attack is clear, while slower or flank-focussed build-up is less potent. Much of the explanation for these differences in conversion rate can be linked to the distance from which such shots are taken on average.

An interesting wrinkle is the similarity in conversion rates between the ‘deep build-up’ and ‘deep fast-attacks’ profiles, with shots taken in the build-up focussed profile being approximately 2 yards further away from goal on average than the faster attacks. Looking through examples of the ‘deep build-up’ attacks, these are often characterised by periods of ball circulation in deeper areas followed by a quick transition through the opposition half towards goal with the opposition defense caught higher up the pitch, which may explain the results somewhat.

Finally, here is a look at how attacking styles have evolved over time. The major changes are the decline in ‘flank-focussed build-up’ and increase in the ‘midfield regain & fast attack’ profile, which is perhaps unsurprising given wider tactical trends and the managerial changes over the period. There is also a trend in attacks from deep being generated from faster-attacks rather than build-up focussed play. A greater emphasis on transitions coupled with fast/direct attacking appears to have emerged across the Premier League.

These are just a few observations and highlights from the presentation and I’ll hopefully put together some more team and player focussed work in the near future. It has been nearly a year since my last post but hopefully I’ll be putting out a steadier stream of content over the coming months.

Leicester City’s rise to the top of the Premier League has led to many an analysis by now. Reasons for their ascent have mainly focused on smart recruitment and their counter-attacking style of play, as well as a healthy dose of luck. While their underlying defensive numbers leave something to be desired, their attack is genuinely good. The pace and directness of their attack has regularly been identified as a key facet of their style by writers with analytical leanings.

Analysis by Daniel Altman has been cited in both the Economist and the Guardian, with the crux being that the ‘key’ to stopping Leicester is to ‘slow them down’. Using slightly different metrics, David Sumpter illustrated this further at the recent Opta Pro Forum and on the Sky Sports website, where his analysis surmised that:

For Leicester, it’s about the speed of the attack.

An obvious and somewhat unaddressed question here is whether the pace of Leicester’s attack is the key to their increased effectiveness this season? Equating style with success in football is often a fraught exercise; the often tedious and pale imitations of Guardiola’s possession-orientated approach being a recent example across football.

Below are a raft of numbers comparing various facets of Leicester’s style and effectiveness this season with last season.

Comparison between Leicester City’s speed of attack and shot profile from ‘fast’ possessions. A possession is a passage of play where a team maintains unbroken control of the ball. Possessions moving at greater than 5 m/s on average are classed as ‘fast’. All values are for open-play possessions only. Data via Opta.

The take home message here is that the average pace of Leicester’s play has barely shifted this season compared to last. Only Burnley in 2014/15 and Aston Villa in 2013/14 have attacked at a greater pace than Leicester this season over the past four years.

The proportion of their shots generated via fast paced possessions has risen this year (from 27.5% to 32.1%) and Leicester currently occupy the top position by this metric over this period. In terms of counter-attacking situations, their numbers have barely changed this season (20.1%) compared to last season (20.8%), with only the aforementioned Aston Villa having a greater proportion (21.3%) than them in my dataset.

What has altered is the effectiveness of their attacks this season, as we can see that their expected goal figures have risen. Below are charts comparing their shots from counter-attacking situations, where we can see more shots in the central zone of the penalty area this season and several better quality chances.

Their improvement this year sees them currently rank first and second in expected goals per game from fast-attacks and counter-attacks respectively over the past four season (THAT Liverpool team rank second and first). Based on my figures, Leicester’s goals from these situations are closely in line with expectations also (N.B. my expected goal model doesn’t explicitly account for counter-attacking moves).

The figure below shows how this has evolved over the past two seasons, where we see fast-attacks helping drive their improved attack at the end of 2014/15, which continued into this season. There has been a gradual decline since an early-season peak, although their expected goals from fast-attacks has reduced more than their overall attacking output in open-play, indicating some compensation from other forms of attack.

Rolling ten-match samples of Leicester City’s expected goals for in 2014/15 and 2015/16. All data is for open-play shots only. Data via Opta.

The effectiveness of these attacks has gone a long way to improving Leicester’s offensive numbers. According to my expected goal figures in open-play, they’ve improved from 0.70 per game to 0.94 per game this season. About half of that improvement has come from ‘fast’ paced possessions, with many of these possessions starting from deep areas in their own half.

Examining the way these chances are being created highlights that Leicester are completing more through-balls during their build-up play this season. The absolute numbers are small, with an increase from 11 to 17 through-balls during ‘fast’ possessions and from 6 to 12 during ‘fast’ possessions from their own half, but they do help to explain the increased effectiveness of their play. Approximately 27% of their shots from counter-attacks include a through-ball during their build-up this season, compared to just 11% last season. Through-balls are an effective means of opening up space and increasing the likelihood of scoring during these fast-paced moves. Leicester’s counter-attacks are also far less reliant on crosses this season, with just 2 of these attacks featuring a cross during build-up compared to 9 last season, which will further increase the likelihood of scoring.

Speed is an illusion. Leicester’s doubly so.

Overall, attacking at pace is a difficult skill to master but the rewards can be high. The pace and verve of Leicester’s attack has been eye-catching but it is the execution of these attacks, rather than the actual speed of them that has been the most important factor. Slowing Leicester down isn’t the key to stopping them, rather the focus should be either on denying them those potential counter-attacking situations or diluting their impact should you find yourself on the receiving end of one.

Whether they can sustain their attacking output from these situations is a difficult question to answer. If we examine how well output is maintained from one year to the next, the correlation for expected goals from counter-attacks is reasonable (0.55), while goal expectation per shot is lower (0.30). Many factors will determine the values here, not least the relatively small number of shots per season of this type, as well as a host of other intrinsic football factors. For fast-attacks, the correlations rise to 0.59 for expected goals and 0.52 for expected goals per shot. For comparison, the values for all open-play shots in my data-set are 0.91 and 0.63.

Examining the data in a little more depth suggests that the better counter-attacking and/or fast-paced teams tend to maintain their output, particularly if they retain managerial and squad continuity. Leicester have a good attack overall that is excellent at exploiting space with fast-attacking moves.

Retaining and perhaps even supplementing their attacking core over the summer would likely go a long way to maintaining a style of play that has brought them rich rewards.

Over on StatsBomb, I’ve written about Leicester’s attacking exploits this season, specifically focusing on the style and effectiveness of their attack. That required a fair amount of research into various aspects relating to the speed and directness of teams attacks, which I’ve looked into since I started looking at possessions and expected goals.

One output of all that is a bunch of numbers at the team and player level stretching back over the past four seasons about fast-attacks and counter-attacks, some of which I will post below along with some comments.

As a brief reminder, a possession is a passage of play where a team maintains unbroken control of the ball. I class a possession moving at greater than 5 m/s on average as ‘fast’ based on looking at a bunch of diagnostics relating to all possessions i.e. not just those ending with a shot. The final number is fairly arbitrary as I just went with a round number rather than a precisely calculated one but the interpretation of the results didn’t shift much when altering the boundary. Looking at the data, there is probably some separation into slow attacks (<2 m/s), medium-paced attacks (2-5 m/s) and then the fast attacks (>5 m/s). Note that some attacks go away from goal, so they end up with a negative speed (technically I’m calculating velocity here but I’ll leave that for another time), so these are attacks towards the goal.

Counter-attacks are when these fast-paced moves begin in a teams own half. Again this is fairly arbitrary from a data point-of-view but it at least fits in with what I think most would consider to be a counter-attack and it’s very easy to split the data into narrower bands in future.

All of the numbers below are based on my expected goals model using open-play shots only. I don’t include a speed of attack or counter-attacking adjustment in my model.

So, without further ado, here are some graphs…

Top-20 offensive fast-attacking teams

Top 20 teams in terms of fast-attacking expected goals for over the past four seasons.

Champions Elect Leicester City sit atop the pile with a reasonable gap on THAT Liverpool team, with a fairly big drop to the chasing pack behind. Arsenal and Manchester City are quite well represented here illustrating the diversity of their attacks – while both are typically among the slowest teams on average, they can step it up effectively when presented with the opportunity.

Top-20 offensive counter-attacking teams

Top 20 teams in terms of counter-attacking expected goals for over the past four seasons.

Number one isn’t a huge shock, with this years Leicester City narrowly ahead of the 12/13 iteration of Liverpool. A lot of the same teams are found in both the fast-attacking and counter-attacking brackets, which isn’t a great surprise perhaps.

Southampton this year are perhaps a little surprising and it is a big shift from previous seasons (0.056-0.075 per game), although I’ll admit I haven’t paid them that much attention this year. Their defense is the 6th worst in this period on counter-attacks also (3rd worst on fast-attacks). When did Southampton become a basketball team?

What is particularly noticeable is the prevalence of teams from the past two seasons in the top-10. A trend towards more-transition orientated play? Something to examine in more detail at another time perhaps.

Top-20 defensive fast-attacking teams

Top 20 teams in terms of fast-attacking expected goals against over the past four seasons.

Most of the best performances on the defensive side are from the 12/13 and 13/14 seasons, which might give some credence to a greater emphasis more recently on transitions along with an inability to cope with them.

The list overall is populated by the relative mainstays of Manchester City, Liverpool and West Brom along with various fingerprints from Mourinho, Warnock and Pulis

Top-20 defensive counter-attacking teams

Top 20 teams in terms of counter-attacking expected goals against over the past four seasons.

Interestingly there is a greater diversity between the counter-attacking and fast-attacking metrics on the defensive side of the ball than on the offensive side, which might point to potential strengths and/or weaknesses in certain teams.

Spurs last season rank as the worst defensive side in terms of counter-attacking expected goals against, and are narrowly beaten into second spot for fast-attacks by the truly awful 2012/13 Reading team.

Top-20 fast-attacking players

Top 20 players in terms of fast-attacking expected goals per 90 minutes over the past four seasons. Minimum 2,700 minutes played.

Lastly, we’ll take a quick look at players. For now, I’m just isolating the player who took the shot, rather than those who participated in the build-up to the goal. A lot of this will be tied up in playing style and team effects.

Jamie Vardy is clearly the standout name here, followed by Daniel Sturridge and Danny Ings. Sturridge leads the chart in terms of actual goals with 0.21 goals per 90 minutes, with Vardy third on 0.18.

Vardy’s overall open-play expected goals per 90 minutes stands at 0.26 by my numbers over the past two seasons, so over half of his xG per 90 comes from getting on the end of fast-attacking moves. He sits in 16th place over all for those with over 2,700 minutes played, which is respectable but he is clearly elite when it comes to faster-paced attacks.

Top-20 counter-attacking players

Top 20 players in terms of counter-attacking expected goals per 90 minutes over the past four seasons. Minimum 2,700 minutes played.

Danny Ings sits on top when it comes to counter-attacking, which bodes well for his future under Jürgen Klopp at Liverpool, providing his injury hasn’t unduly affected him. Again, Sturridge leads the list in terms of actual goals with 0.13 per 90 minutes, with Vardy second on 0.12. The sample sizes are lower here, so we would expect a greater degree of variance in terms of the comparison between reality and expectation.

One of the interesting things when comparing these lists is the divergence and/or similarities between the overall goal scorer chart. For example, Edin Džeko and Wilfried Bony sit in first and fourth place respectively in the overall table for this period but lie outside the top-20 when it comes to faster-paced attacks. A clear application of this type of work is player profiling to fit the particular style and needs of a prospective team, which Paul Riley has previously shown to be a useful method for evaluating forwards.

Moving forward

I wanted to post these as a starting point for discussion before I drill down further into the details in the future. The data presented here and that underlying it are very rich in detail and potential applications, which I have already started to explore. In particular, there is a lot of spatial information encapsulated in the data that can inform how teams attack and defend, which can help to build further descriptive elements to team styles along side measures of their effectiveness.

Football is a complex game that has many facets that are tough to represent with numbers. As far as public analytics goes, the metrics available are best at assessing team strength, while individual player assessments are strongest for attacking players due to their heavy reliance on counting statistics relating to on-the-ball numbers. This makes assessing defenders and goalkeepers a particular challenge as we miss the off-ball positional adjustments and awareness that marks out the best proponents of the defensive side of the game.

One potential avenue is to examine metrics from a ‘top-down’ perspective i.e. we look at overall results and attempt to untangle how a player contributed to that result. This has the benefit of not relying on the incomplete picture provided by on-ball statistics but we do lose process level information on how a player contributes to overall team performance (although we could use other methods to investigate this).

As far as football is concerned, there are a few methods that aim to do this, with Goalimpact being probably the most well-known. Goalimpact attempts to measure ‘the extent that a player contributes to the goal difference per minute of a team’ via a complex method and impressively broad dataset. Daniel Altman has a metric based on ‘Shapley‘ values that looks at how individual players contribute to the expected goals created and conceded while playing.

Outside of football, one of the most popular statistics to measure player contribution to overall results is the concept of plus-minus (or +/-) statistics, which is commonly used within basketball, as well as ice hockey. The most basic of these metrics simply counts the goals or points scored and conceded while a player is on the pitch and comes up with an overall number to represent their contribution. There are many issues with such an approach, such as who a player is playing along side, their opponent and the venue of a match; James Grayson memorably illustrated some of these issues within football when WhoScored claimed that Barcelona were a better team without Xavi Hernández.

Several methods exist in other sports to control for these factors (basically they add in a lot more maths) and some of these have found their way to football. Ford Bohrmann and Howard Hamilton had a crack at the problem here and here respectively but found the results unsatisfactory. Martin Eastwood used a Bayesian approach to rate players based on the goal difference of their team while they are playing, which came up with more encouraging results.

Expected goals

One of the potential issues with applying plus-minus to football is the low scoring nature of the sport. A heavily influential player could play a run of games where his side can’t hit the proverbial barn door, whereas another player could be fortunate to play during a hot-streak from one of his fellow players. Goal-scoring is noisy in football, so perhaps we can utilise a measure that irons out some of this noise but still represents a good measure of team performance. Step forward expected goals.

Instead of basing the plus-minus calculation on goals, I’ve used my non-shot expected goal numbers as the input. The method splits each match into separate periods and logs which players are on the pitch at a given time. A new segment starts when a lineup changes i.e. when a substitution occurs or a player is sent off. The expected goals for each team are then calculated for each period and converted to a value per 90 minutes. Each player is a ‘variable’ in the equation, with the idea being that their contribution to a teams expected goal difference can be ‘solved’ via the regression equation.

For more details on the maths side of plus-minus, I would recommend checking out Howard Hamilton’s article. I used ridge regression, which is similar to linear regression but the calculated coefficients tend to be pulled towards zero (essentially it increases bias while limiting huge outliers, so there is a tradeoff between bias and variance).

As a first step, I’ve calculated the plus-minus figures over the previous three English Premier League seasons (2012/13 to 2014/15). Every player that has appeared in the league is included as I didn’t find there was much difference when excluding players under a certain threshold of minutes played (this also avoids having to include such players in some other manner, which is typically done in basketball plus-minus). However, estimates for players with fewer than approximately 900 minutes played are less robust.

The chart below shows the proportion of players with a certain plus-minus score per 90 minutes played. As far as interpretation goes, if we took a team made up of 11 players, each with a plus-minus score of zero, the expected goal difference of the team would add up to zero. If we then replaced one of the players with one with a plus-minus of 0.10, the team’s expected goal difference would be raised to 0.10.

Distribution of xG plus-minus scores.

The range of plus-minus scores is from -0.15 to 0.15, so replacing a player with a plus-minus score of zero with one with a score of 0.15 would equate to an extra 5.7 goals over a Premier League season. Based on this analysis by James Grayson, that would equate to approximately 3.5-4.0 points over a season on average. This is comparable to figures published relating to calculations based on the Goalimpact metric system discussed earlier. That probably seems a little on the low side for what we might generally assume would be the impact of a single player, which could point towards the method either narrowing the distribution too much (my hunch) or an overestimate in our intuition. Validation will have to wait for another day

Most valuable players

Below is a table of the top 13 players according to the model. Vincent Kompany is ranked the highest by this method; on one hand this is surprising given the often strong criticism that he receives but then on the other, when he is missing, those replacing him in Manchester City’s back-line look far worse and the team overall suffers. According to my non-shots xG model, Manchester City have been comfortably the best team over the previous three seasons and are somewhat accordingly well-represented here.

Top 13 players by xG plus-minus scores for the 2012/13-2014/15 Premier League seasons. Minimum minutes played was 3420 i.e. equivalent to a full 38 match season.

Probably the most surprising name on the list is at number three…step forward Joe Allen! I doubt even Joe’s closest relatives would rate him as the third best player in the league but I think that what the model is trying to say here is that Allen is a very valuable cog who improves the overall performance level of the team. Framed in that way, it is perhaps slightly more believable (if only slightly) that his skill set gets more out of his team mates. When fit, Allen does bring added intelligence to the team and as a Liverpool fan, ‘intelligence’ isn’t usually a word I associate with the side. Highlighting players who don’t typically stand-out is one of the goals of this sort of analysis, so I’ll run with it for now while maintaining a healthy dose of skepticism.

I chose 13 as the cutoff in the table so that the top goalkeeper on the list, Hugo Lloris, is included so that an actual team could be put together. Note that this doesn’t factor in shot-stopping (I’ve actually excluded rebound shots, which might have been one way for goalkeepers to influence the scores more directly), so the rating for goalkeepers should be primarily related to other aspects of goalkeeping skills. Goalkeepers are probably still quite difficult to nail down with this method due to them rarely missing matches though, so there is a fairly large caveat with their ratings.

Being as this is just an initial look, I’m going to hold off on putting out a full list but I definitely will do in time once I’ve done some more validation work and ironed out some kinks.

Validation, Repeatability & Errors

Fairly technical section. You’ve been warned.

One of the key facets of using ridge regression is choosing a ‘suitable’ regularization parameter, which is what controls the bias-to-variance tradeoff; essentially larger values will pull the scores closer to zero. Choosing this objectively is difficult and in reality, some level of subjectivity is going to be involved at some stage of the analysis. I did A LOT of cross-validation analysis where I split the match segments into even and odd sets and ran the regression while varying a bunch of parameters (e.g. minutes cutoff, weighting of segment length, the regularization value). I then looked at the error between the regression coefficients (the player plus-minus scores) in the out-of-sample set compared to the in-sample set to choose my parameters. For the regularization parameter, I chose a value of 50 as that was where the error reached a minimum initially with relatively little change for larger values.

I also did some repeatability testing comparing consecutive seasons. As is common with plus-minus, the repeatability is very limited. That isn’t much of a surprise as the method is data-hungry and a single season doesn’t really cut it for most players. The bias introduced by the regularization doesn’t help either here. I don’t think that this is a death-knell for the method though, given the challenges involved and the limitations of the data.

In the table above, you probably noticed I included a column for errors, specifically the standard error. Typically, this has been where plus-minus has fallen down, particularly in relation to football. Simply put, the errors have been massive and have rendered interpretation practically impossible e.g. the errors for even the most highly rated players have been so large that statistically speaking it has been difficult to evaluate whether a player is even ‘above-average’.

I calculated the errors from the ridge regression via bootstrap resampling. There are some issues with combining ridge regression and bootstrapping (see discussion here and page 18 here) but these errors should give us some handle on the variability in the ratings.

You can see above that the errors are reasonably large, so the separation between players isn’t as good as you would want. In terms of their magnitude relative to the average scores, the errors are comparable to those I’ve found published for basketball. That provides some level of confidence as they’ve been demonstrated to have genuine utility there. Note that I’ve not cherry-picked the players above in terms of their standard errors either; encouragingly the errors don’t show any relationship with minutes played after approximately 900 minutes.

The gold road’s sure a long road

That is essentially it so far in terms of what I’m ready to share publicly. In terms of next steps, I want to expand this to include other leagues so that the model can keep track of players transferring in and out of a league. For example, Luis Suárez disappears when the model reaches the 2014/15 season, when in reality he was settling in quite nicely at Barcelona. That likely means that his rating isn’t a true reflection of his overall level over the period.

Evaluating performance over time is also a big thing I want to be able to do; a three year average is probably not ideal, so either some weighting for more recent seasons or a moving two season window would be better. This is typically what has been done in basketball and based on initial testing, it doesn’t appear to add more noise to the results.

Validating the ratings in some fashion is going to be a challenge but I have some ideas on how to go about that. One of the advantages of plus-minus style metrics is that they break-down team level performance to the player level, which is great as it means that adding the players back up into a team or squad essentially correlates perfectly with team performance (as represented by expected goals here). However, that does result in a tautology if the validation is based on evaluating team performance unless there are fundamental shifts in team makeup e.g. a large number of transfers in and out of a squad or injuries to key personnel.

This is just a start, so there will be more to come over time. The aim isn’t to provide a perfect representation of player contribution but to add an extra viewpoint to squad and player evaluation. Combining it with other data analysis and scouting would be the longer-term goal.

I’ll leave you with piano carrier extradionaire, Joe Allen.

Joe Allen on hearing that he is Liverpool’s most important player over the past three years.

The narrative surrounding Arsenal has been strong this week, with their fall to fourth place in the table coming on Groundhog Day no less. This came despite a strong second half showing against Southampton, with Fraser Forster denying them. Arsenal’s season has been characterised by several excellent performances in terms of expected goals but the scoreline hasn’t always reflected their statistical dominance. Colin Trainor illustrated their travails in front of goal in this tweet.

I wrote in this post on how Arsenal’s patient approach eschews more speculative shots in search of high quality chances and that this was seemingly more pronounced this season. Arsenal are highly rated by expected goal models this season but traditional shot metrics are nowhere near as convinced.

Analytical folk will point to the high quality of Arsenal’s shots this season to explain the difference, where quality is denoted by the average probability that a shot will be scored. For example, a team with an average shot quality of 0.10 would ‘expect’ to score around 10% of their shots taken.

In the chart below, I’ve looked at the full distribution of Arsenal’s shots in open-play this season in terms of ‘shot quality’ and compared them with their previous incarnations and peers from the 2012/13 season through to the present. Looking at shot quality in this manner illustrates that the majority of shots are of relatively low quality (less than 10% chance of being scored) and that the distribution is heavily-skewed.

Proportion of total shots in open-play according to the probability of them being scored (expected goals per shot). Grey lines are non-Arsenal teams from the English Premier League from 2012/13 to the present. Blue lines are previous Arsenal teams, while red is Arsenal from this season. Data via Opta.

In terms of Arsenal, what stands out here is that their current incarnation are taking a smaller proportion of ‘low-quality’ shots (those with an expected goal estimate from 0-0.1) than any previous team by a fairly wide margin. At present, 59% of Arsenal’s shots reside in this bracket, with the next lowest sitting at 64%. Their absolute number of shots in this bracket has also fallen compared to previous seasons.

Moving along the scale, Arsenal reside along the upper edge in terms of these higher quality shots and actually have the largest proportion in the 0.2-0.3 and 0.3-0.4 ranges. As you would expect, they’ve traded higher quality shots for lower quality efforts according to the data.

Arsenal typically post above average shot quality figures but the shift this season appears to be significant. The question is why?

Mesut Özil?

One big change this season is the sustained presence (and excellence) of Mesut Özil; so far this season he has made 22 appearances (playing in 88% of available minutes) compared to 22 appearances last season (54%) and 26 matches in his debut season (63%). According to numbers from the Football in the Clouds website, his contribution to Arsenal’s shots while he is on the pitch is at 40% compared to 30% in 2014/15. Daniel Altman also illustrated Özil’s growing influence in his post in December.

Özil is the star that Arsenal’s band of attacking talent orbits, so it is possible that he is driving this focus on quality via his creative skills. His attacking contribution in terms of shots and shot-assists is among the highest in the league but is heavily-skewed towards assisting others, which is unusual among high-volume contributors.

Looking at the two previous seasons though, there doesn’t appear to be any great shift in Arsenal’s shot quality during the periods when Özil was out of the team through injury. His greater influence and regular presence in the side this season has probably shifted the dial but quantifying how much would require further analysis.

Analytics?

Another potential driver could be that Wenger and his coaching staff have attempted to adjust Arsenal’s tactics/style with a greater focus on quality.

Below is a table of Arsenal’s ‘volume’ shooters over the past few seasons, where I’ve listed their number of shots from outside of the box per 90 minutes and the proportion of their shots from outside the box. Note that these are for all shots, so set-pieces are included but it shouldn’t skew the story too much.

The general trend is that Arsenal’s players have been taking fewer shots from outside of the box this season compared to previous and that there has been a decline proportionally for most players also. Some of that may be driven by changing roles/positions in the team but there appears to be a clear shift in their shot profiles. Giroud for example has taken just 3 shots from outside the box this season, which is in stark contrast to his previous profile.

Given the data I’ve already outlined, the above isn’t unexpected but then we’re back to the question of why?

Wenger has mentioned expected goals on a few occasions now and has reportedly been working more closely with the analytics team that Arsenal acquired in 2012. Given his history and reputation, we can be relatively sure that Wenger would appreciate the merits of shot quality; could the closer working relationship and trust developed with the analytics team have led to him placing an even greater emphasis on seeking better shooting opportunities?

The above is just a theory but the shift in emphasis does appear to be significant and is an interesting feature to ponder.

Adjusted expectations?

Whatever has driven this shift in Arsenal’s shot profile, the change is quite pronounced. From an opposition strategy perspective, this presents an interesting question: if you’re aware of this shift in emphasis, whether through video analysis or data, do you alter your defensive strategy accordingly?

While Arsenal’s under-performance in terms of goals versus expected goals currently looks like a case of variance biting hard, could this be prolonged if their opponents adjust? It doesn’t look like their opponents have altered tactics thus far based on examining the data but having shifted the goalposts in terms of shot quality, could this be their undoing?